A critical analysis of residential flood damage estimation curves

McBean, Edward A.; Fortin, Michael; Gorrie, Jack

doi:10.1139/l86-012

Cited by 20 publications

(21 citation statements)

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“…Results of these validation tests illustrate the importance of model calibration, especially when the water depth is the only hydraulic parameter taken into account (Cammerer et al, 2013;Chang et al, 2008;McBean et al, 1986). In other words, flood damage, being a complicated process, could be dependent on more damage-influencing parameters than those considered here (Fuchs et al, 2011;Grahn and Nyberg, 2014;Hasanzadeh Nafari et al, 2016c;Merz et al, 2013;Schröter et al, 2014).…”

Section: Results Comparison and Model Validationmentioning

confidence: 83%

“…Figure 6. Comparison of the flood damage estimation models' precision per water-depth class (RMSE: root mean square error; number of samples for each sub-class of water depth, respectively, is 14, 36, 52, 96, 125, 222, and 68). of study (Hasanzadeh Nafari et al, 2016b;McBean et al, 1986).…”

Section: Results Comparison and Model Validationmentioning

confidence: 97%

“…Empirical curves are drawn based on actual data collected from one specific event. Due to the differences in flood and building characteristics, they cannot be directly employed in different times and places (Gissing and Blong, 2004;McBean et al, 1986). To resolve this issue, general synthetic curves based on a valuation survey have been created for different types of buildings.…”

Section: Introductionmentioning

confidence: 99%

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Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

Nafari

Amadio

Ngo

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. The damage triggered by different flood events costs the Italian economy millions of euros each year. This cost is likely to increase in the future due to climate variability and economic development. In order to avoid or reduce such significant financial losses, risk management requires tools which can provide a reliable estimate of potential flood impacts across the country. Flood loss functions are an internationally accepted method for estimating physical flood damage in urban areas. In this study, we derived a new flood loss function for Italian residential structures (FLF-IT), on the basis of empirical damage data collected from a recent flood event in the region of Emilia-Romagna. The function was developed based on a new Australian approach (FLFA), which represents the confidence limits that exist around the parameterized functional depth-damage relationship. After model calibration, the performance of the model was validated for the prediction of loss ratios and absolute damage values. It was also contrasted with an uncalibrated relative model with frequent usage in Europe. In this regard, a threefold cross-validation procedure was carried out over the empirical sample to measure the range of uncertainty from the actual damage data. The predictive capability has also been studied for some sub-classes of water depth. The validation procedure shows that the newly derived function performs well (no bias and only 10 % mean absolute error), especially when the water depth is high. Results of these validation tests illustrate the importance of model calibration. The advantages of the FLF-IT model over other Italian models include calibration with empirical data, consideration of the epistemic uncertainty of data, and the ability to change parameters based on building practices across Italy.

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Section: Results Comparison and Model Validationmentioning

confidence: 83%

Section: Results Comparison and Model Validationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

Nafari

Amadio

Ngo

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…However, results of the GA and USACE models do not lie within the confidence interval of the reported loss and their performance is rejected in this area of study. This issue and the validation procedure illustrates the importance of model calibration with the empirical local data sets, particularly when the water depth is the only hydraulic factor considered (Cammerer et al, 2013;Chang et al, 2008;McBean et al, 1986).…”

Section: Results Comparison and Model Validation For The Maranoa Studymentioning

confidence: 99%

“…Damage surveys after a flood are not a common activity for governments, and they mostly rely on insurance company payouts or media reports for information (Bureau of Transport Economics, 2001;McBean et al, 1986;Merz et al, 2010;Smith, 1994). Insurance companies are mainly concerned with the collection of data on repair costs and their relation to the total insured value of the flooded object.…”

Section: Official Flood Loss Datamentioning

confidence: 99%

Calibration and validation of FLFA<sub>rs</sub> -- a new flood loss function for Australian residential structures

Nafari¹,

Ngo

Lehman

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. Rapid urbanisation, climate change and unsustainable developments are increasing the risk of floods. Flood is a frequent natural hazard that has significant financial consequences for Australia. The emergency response system in Australia is very successful and has saved many lives over the years. However, the preparedness for natural disaster impacts in terms of loss reduction and damage mitigation has been less successful.In this paper, a newly derived flood loss function for Australian residential structures (FLFA rs ) has been presented and calibrated by using historic data collected from an extreme event in Queensland, Australia, that occurred in 2013. Afterwards, the performance of the method developed in this work (contrasted to one Australian model and one model from USA) has been compared with the observed damage data collected from a 2012 flood event in Maranoa, Queensland. Based on this analysis, validation of the selected methodologies has been performed in terms of Australian geographical conditions.Results obtained from the new empirically based function (FLFA rs ) and the other models indicate that it is apparent that the precision of flood damage models is strongly dependent on selected stage damage curves, and flood damage estimation without model calibration might result in inaccurate predictions of losses. Therefore, it is very important to be aware of the associated uncertainties in flood risk assessment, especially if models have not been calibrated with real damage data.

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Quantitative multi-risk analysis for natural hazards: a framework for multi-risk modelling

et al. 2011

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This paper introduces a generic framework for multi-risk modelling developed in the project 'Regional RiskScape' by the Research Organizations GNS Science and the National Institute of Water and Atmospheric Research Ltd. (NIWA) in New Zealand. Our goal was to develop a generic technology for modelling risks from different natural hazards and for various elements at risk. The technical framework is not dependent on the specific nature of the individual hazard nor the vulnerability and the type of the individual assets. Based on this generic framework, a software prototype has been developed, which is capable of 'plugging in' various natural hazards and assets without reconfiguring or adapting the generic software framework. To achieve that, we developed a set of standards for treating the fundamental components of a risk model: hazards, assets (elements at risk) and vulnerability models (or fragility functions). Thus, the developed prototype system is able to accommodate any hazard, asset or fragility model, which is provided to the system according to that standard. The software prototype was tested by modelling earthquake, volcanic ashfall, flood, wind, and tsunami risks for several urban centres and small communities in New Zealand.

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A critical analysis of residential flood damage estimation curves

Cited by 20 publications

References 1 publication

Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

Calibration and validation of FLFA<sub>rs</sub> -- a new flood loss function for Australian residential structures

Quantitative multi-risk analysis for natural hazards: a framework for multi-risk modelling

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A critical analysis of residential flood damage estimation curves

Cited by 20 publications

References 1 publication

Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

Calibration and validation of FLFA&lt;sub&gt;rs&lt;/sub&gt; -- a new flood loss function for Australian residential structures

Quantitative multi-risk analysis for natural hazards: a framework for multi-risk modelling

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Calibration and validation of FLFA<sub>rs</sub> -- a new flood loss function for Australian residential structures